code: def main():
      tokenizer = AutoTokenizer.from_pretrained(
          "t5-small", padding=True
      )
      tokenizer.save_pretrained(
          "./MLDATA/NLP/hf_tokenizers"
      )  # CHANGE DIRECTORY
  
      prefix = "<2en>"
      source_lang = "de"
      target_lang = "en"
      remote_data = load_dataset("news_commentary", "de-en")
  
      def preprocess_function(examples):
          inputs = [
              prefix + example[source_lang]
              for example in examples["translation"]
          ]
          targets = [
              example[target_lang]
              for example in examples["translation"]
          ]
          model_inputs = tokenizer(
              inputs, max_length=128, truncation=True
          )
          with tokenizer.as_target_tokenizer():
              labels = tokenizer(
                  targets, max_length=128, truncation=True
              )
          model_inputs["labels"] = labels["input_ids"]
          return model_inputs
  
      remote_data = remote_data.map(preprocess_function, batched=True)
      model = T5ForConditionalGeneration.from_pretrained("t5-small")
      remote_data = remote_data.remove_columns(["id", "translation"])
      remote_data.set_format(type="torch")
      data_collator = CustomDataCollatorSeq2SeqBeta(
          tokenizer=tokenizer, model=model
      )
  
      train_exps = []
      for i in range(0, 2):
          # We use very small experiences only to showcase the library.
          # Adapt this to your own benchmark
          exp_data = remote_data["train"].select(
              range(30 * i, 30 * (i + 1))
          )
          tl = DataAttribute(
              ConstantSequence(i, len(exp_data)), "targets_task_labels"
          )
  
          exp = CLExperience()
          exp.dataset = AvalancheDataset(
              [exp_data], data_attributes=[tl], collate_fn=data_collator
          )
          train_exps.append(exp)
  
      benchmark = CLScenario(
          [
              CLStream("train", train_exps),
              # add more stream here (validation, test, out-of-domain, ...)
          ]
      )
      eval_plugin = avalanche.training.plugins.EvaluationPlugin(
          avalanche.evaluation.metrics.loss_metrics(
              epoch=True, experience=True, stream=True
          ),
          loggers=[avalanche.logging.InteractiveLogger()],
          strict_checks=False,
      )
      plugins = [ReplayPlugin(mem_size=200)]
      optimizer = torch.optim.Adam(model.parameters(), lr=2)
      strategy = HGNaive(
          model,
          optimizer,
          torch.nn.CrossEntropyLoss(ignore_index=-100),
          evaluator=eval_plugin,
          train_epochs=1,
          train_mb_size=10,
          plugins=plugins,
      )
      for experience in benchmark.train_stream:
          strategy.train(experience, collate_fn=data_collator)
          strategy.eval(benchmark.train_stream)
  
imports: from avalanche.benchmarks.utils import DataAttribute, ConstantSequence
  from avalanche.training.plugins import ReplayPlugin
  from dataclasses import dataclass
  from transformers import PreTrainedTokenizerBase
  from typing import Optional, Union, Any
  from transformers.utils import PaddingStrategy
  import torch
  import avalanche
  import torch.nn
  from avalanche.benchmarks import CLScenario, CLStream, CLExperience
  from avalanche.evaluation.metrics import accuracy_metrics
  import avalanche.training.templates.base
  from avalanche.benchmarks.utils import AvalancheDataset
  from transformers import AutoTokenizer
  from transformers import T5ForConditionalGeneration
  from datasets import load_dataset
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: main() and returns: model_inputs